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    "# Method Chaining\n",
    "\n",
    "Method chaining, where you call methods on an object one after another, is in vogue at the moment.\n",
    "It's always been a style of programming that's been possible with pandas,\n",
    "and over the past several releases, we've added methods that enable even more chaining.\n",
    "\n",
    "- [assign](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.assign.html) (0.16.0): For adding new columns to a DataFrame in a chain (inspired by dplyr's `mutate`)\n",
    "- [pipe](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pipe.html) (0.16.2): For including user-defined methods in method chains.\n",
    "- [rename](http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#changes-to-rename) (0.18.0): For altering axis names (in additional to changing the actual labels as before).\n",
    "- [Window methods](http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#window-functions-are-now-methods) (0.18): Took the top-level `pd.rolling_*` and `pd.expanding_*` functions and made them `NDFrame` methods with a `groupby`-like API.\n",
    "- [Resample](http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#resample-api) (0.18.0) Added a new `groupby`-like API\n",
    "- [.where/mask/Indexers accept Callables](https://github.com/pydata/pandas/pull/12539) (0.18.1): In the next release you'll be able to pass a callable to the indexing methods, to be evaluated within the DataFrame's context (like `.query`, but with code instead of strings).\n",
    "\n",
    "My scripts will typically start off with large-ish chain at the start getting things into a manageable state.\n",
    "It's good to have the bulk of your munging done with right away so you can start to do Science™:\n",
    "\n",
    "Here's a quick example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style='ticks', context='talk')\n",
    "\n",
    "import prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 450017 entries, 0 to 450016\n",
      "Data columns (total 33 columns):\n",
      "fl_date                  450017 non-null datetime64[ns]\n",
      "unique_carrier           450017 non-null category\n",
      "airline_id               450017 non-null int64\n",
      "tail_num                 449378 non-null category\n",
      "fl_num                   450017 non-null int64\n",
      "origin_airport_id        450017 non-null int64\n",
      "origin_airport_seq_id    450017 non-null int64\n",
      "origin_city_market_id    450017 non-null int64\n",
      "origin                   450017 non-null category\n",
      "origin_city_name         450017 non-null object\n",
      "dest_airport_id          450017 non-null int64\n",
      "dest_airport_seq_id      450017 non-null int64\n",
      "dest_city_market_id      450017 non-null int64\n",
      "dest                     450017 non-null category\n",
      "dest_city_name           450017 non-null object\n",
      "crs_dep_time             450017 non-null datetime64[ns]\n",
      "dep_time                 441445 non-null datetime64[ns]\n",
      "dep_delay                441476 non-null float64\n",
      "taxi_out                 441244 non-null float64\n",
      "wheels_off               441244 non-null float64\n",
      "wheels_on                440746 non-null float64\n",
      "taxi_in                  440746 non-null float64\n",
      "crs_arr_time             450017 non-null datetime64[ns]\n",
      "arr_time                 440555 non-null datetime64[ns]\n",
      "arr_delay                439645 non-null float64\n",
      "cancelled                450017 non-null float64\n",
      "cancellation_code        8886 non-null category\n",
      "carrier_delay            97699 non-null float64\n",
      "weather_delay            97699 non-null float64\n",
      "nas_delay                97699 non-null float64\n",
      "security_delay           97699 non-null float64\n",
      "late_aircraft_delay      97699 non-null float64\n",
      "unnamed: 32              0 non-null float64\n",
      "dtypes: category(5), datetime64[ns](5), float64(13), int64(8), object(2)\n",
      "memory usage: 103.2+ MB\n"
     ]
    }
   ],
   "source": [
    "def read(fp):\n",
    "    df = (pd.read_csv(fp)\n",
    "            .rename(columns=str.lower)\n",
    "            .drop('unnamed: 36', axis=1)\n",
    "            .pipe(extract_city_name)\n",
    "            .pipe(time_to_datetime, ['dep_time', 'arr_time', 'crs_arr_time', 'crs_dep_time'])\n",
    "            .assign(fl_date=lambda x: pd.to_datetime(x['fl_date']),\n",
    "                    dest=lambda x: pd.Categorical(x['dest']),\n",
    "                    origin=lambda x: pd.Categorical(x['origin']),\n",
    "                    tail_num=lambda x: pd.Categorical(x['tail_num']),\n",
    "                    unique_carrier=lambda x: pd.Categorical(x['unique_carrier']),\n",
    "                    cancellation_code=lambda x: pd.Categorical(x['cancellation_code'])))\n",
    "    return df\n",
    "\n",
    "def extract_city_name(df):\n",
    "    '''\n",
    "    Chicago, IL -> Chicago for origin_city_name and dest_city_name\n",
    "    '''\n",
    "    cols = ['origin_city_name', 'dest_city_name']\n",
    "    city = df[cols].apply(lambda x: x.str.extract(\"(.*), \\w{2}\", expand=False))\n",
    "    df = df.copy()\n",
    "    df[['origin_city_name', 'dest_city_name']] = city\n",
    "    return df\n",
    "\n",
    "def time_to_datetime(df, columns):\n",
    "    '''\n",
    "    Combine all time items into datetimes.\n",
    "\n",
    "    2014-01-01,0914 -> 2014-01-01 09:14:00\n",
    "    '''\n",
    "    df = df.copy()\n",
    "    def converter(col):\n",
    "        timepart = (col.astype(str)\n",
    "                       .str.replace('\\.0$', '')  # NaNs force float dtype\n",
    "                       .str.pad(4, fillchar='0'))\n",
    "        return pd.to_datetime(df['fl_date'] + ' ' +\n",
    "                               timepart.str.slice(0, 2) + ':' +\n",
    "                               timepart.str.slice(2, 4),\n",
    "                               errors='coerce')\n",
    "    df[columns] = df[columns].apply(converter)\n",
    "    return df\n",
    "\n",
    "output = 'data/flights.h5'\n",
    "\n",
    "if not os.path.exists(output):\n",
    "    df = read(\"data/627361791_T_ONTIME.csv\")\n",
    "    df.to_hdf(output, 'flights', format='table')\n",
    "else:\n",
    "    df = pd.read_hdf(output, 'flights', format='table')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I find method chains readable, though some people don't.\n",
    "Both the code and the flow of execution are from top to bottom, and the function parameters are always near the function itself, unlike with heavily nested function calls.\n",
    "\n",
    "My favorite example demonstrating this comes from [Jeff Allen](http://trestletech.com/wp-content/uploads/2015/07/dplyr.pdf) (pdf). Compare these two ways of telling the same story:\n",
    "\n",
    "```R\n",
    "tumble_after(\n",
    "    broke(\n",
    "        fell_down(\n",
    "            fetch(went_up(jack_jill, \"hill\"), \"water\"),\n",
    "            jack),\n",
    "        \"crown\"),\n",
    "    \"jill\"\n",
    ")\n",
    "```\n",
    "\n",
    "and\n",
    "\n",
    "```R\n",
    "jack_jill %>%\n",
    "    went_up(\"hill\") %>%\n",
    "    fetch(\"water\") %>%\n",
    "    fell_down(\"jack\") %>%\n",
    "    broke(\"crown\") %>%\n",
    "    tumble_after(\"jill\")\n",
    "```\n",
    "\n",
    "Even if you weren't aware that in R `%>%` (pronounced *pipe*) calls the function on the right with the thing on the left as an argument, you can still make out what's going on. Compare that with the first style, where you need to unravel the code to figure out the order of execution and which arguments are being passed where.\n",
    "\n",
    "Admittedly, you probably wouldn't write the first one.\n",
    "It'd be something like\n",
    "\n",
    "```python\n",
    "on_hill = went_up(jack_jill, 'hill')\n",
    "with_water = fetch(on_hill, 'water')\n",
    "fallen = fell_down(with_water, 'jack')\n",
    "broken = broke(fallen, 'jack')\n",
    "after = tmple_after(broken, 'jill')\n",
    "```\n",
    "\n",
    "I don't like this version because I have to spend time coming up with appropriate names for variables.\n",
    "That's bothersome when we don't *really* care about the `on_hill` variable. We're just passing it into the next step.\n",
    "\n",
    "A fourth way of writing the same story may be available. Suppose you owned a `JackAndJill` object, and could define the methods on it. Then you'd have something like R's `%>%` example.\n",
    "\n",
    "```python\n",
    "jack_jill = JackAndJill()\n",
    "(jack_jill.went_up('hill')\n",
    "    .fetch('water')\n",
    "    .fell_down('jack')\n",
    "    .broke('crown')\n",
    "    .tumble_after('jill')\n",
    ")\n",
    "```\n",
    "\n",
    "But the problem is you don't own the `ndarray` or `DataFrame` or [`DataArray`](http://xarray.pydata.org/en/stable/data-structures.html#dataarray), and the exact method you want may not exist.\n",
    "Monekypatching on your own methods is fragile.\n",
    "It's not easy to correctly subclass pandas' DataFrame to extend it with your own methods.\n",
    "Composition, where you create a class that holds onto a DataFrame internally, may be fine for your own code, but it won't interact well with the rest of the ecosystem so your code will be littered with lines extracting and repacking the underlying DataFrame.\n",
    "\n",
    "Perhaps you could submit a pull request to pandas implementing your method.\n",
    "But then you'd need to convince the maintainers that it's broadly useful enough to merit its inclusion (and worth their time to maintain it). And `DataFrame` has something like 250+ methods, so we're reluctant to add more.\n",
    "\n",
    "Enter [`DataFrame.pipe`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pipe.html). All the benefits of having your specific function as a method on the DataFrame, without us having to maintain it, and without it overloading the already large pandas API. A win for everyone.\n",
    "\n",
    "```python\n",
    "jack_jill = pd.DataFrame()\n",
    "(jack_jill.pipe(went_up, 'hill')\n",
    "    .pipe(fetch, 'water')\n",
    "    .pipe(fell_down, 'jack')\n",
    "    .pipe(broke, 'crown')\n",
    "    .pipe(tumble_after, 'jill')\n",
    ")\n",
    "```\n",
    "\n",
    "This really is just right-to-left function execution. The first argument to `pipe`, a callable, is called with the DataFrame on the left as its first argument, and any additional arguments you specify.\n",
    "\n",
    "I hope the analogy to data analysis code is clear.\n",
    "Code is read more often than it is written.\n",
    "When you or your coworkers or research partners have to go back in two months to update your script, having the story of raw data to results be told as clearly as possible will save you time."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Costs\n",
    "\n",
    "One drawback to excessively long chains is that debugging can be harder.\n",
    "If something looks wrong at the end, you don't have intermediate values to inspect. There's a close parallel here to python's generators. Generators are great for keeping memory consumption down, but they can be hard to debug since values are consumed.\n",
    "\n",
    "For my typical exploratory workflow, this isn't really a big problem. I'm working with a single dataset that isn't being updated, and the path from raw data to usuable data isn't so large that I can't drop an `import pdb; pdb.set_trace()` in the middle of my code to poke around.\n",
    "\n",
    "For large workflows, you'll probably want to move away from pandas to something more structured, like [Airflow](http://pythonhosted.org/airflow/) or [Luigi](http://luigi.readthedocs.org/en/stable/index.html).\n",
    "\n",
    "When writing medium sized [ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) jobs in python that will be run repeatedly, I'll use decorators to inspect and log properties about the DataFrames at each step of the process."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "from functools import wraps\n",
    "import logging\n",
    "\n",
    "def log_shape(func):\n",
    "    @wraps(func)\n",
    "    def wrapper(*args, **kwargs):\n",
    "        result = func(*args, **kwargs)\n",
    "        logging.info(\"%s,%s\" % (func.__name__, result.shape))\n",
    "        return result\n",
    "    return wrapper\n",
    "\n",
    "def log_dtypes(func):\n",
    "    @wraps(func)\n",
    "    def wrapper(*args, **kwargs):\n",
    "        result = func(*args, **kwargs)\n",
    "        logging.info(\"%s,%s\" % (func.__name__, result.dtypes))\n",
    "        return result\n",
    "    return wrapper\n",
    "\n",
    "\n",
    "@log_shape\n",
    "@log_dtypes\n",
    "def load(fp):\n",
    "    df = pd.read_csv(fp, index_col=0, parse_dates=True)\n",
    "\n",
    "@log_shape\n",
    "@log_dtypes\n",
    "def update_events(df, new_events):\n",
    "    df.loc[new_events.index, 'foo'] = new_events\n",
    "    return df\n",
    "```\n",
    "\n",
    "This plays nicely with [`engarde`](http://engarde.readthedocs.org), a little library I wrote to validate data as it flows through the pipeline (it essentialy turns those logging statements into excpetions if something looks wrong)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inplace?\n",
    "\n",
    "Most pandas methods have an `inplace` keyword that's `False` by default.\n",
    "In general, you shouldn't do inplace operations.\n",
    "\n",
    "First, if you like method chains then you simply can't use inplace since the return value is `None`, terminating the chain.\n",
    "\n",
    "Second, I suspect people have a mental model of `inplace` operations happening, you know, inplace. That is, extra memory doesn't need to be allocated for the result. [But that might not actually be true](http://stackoverflow.com/a/22533110).\n",
    "Quoting Jeff Reback from that answer\n",
    "\n",
    "> Their is **no guarantee** that an inplace operation is actually faster. Often they are actually the same operation that works on a copy, but the top-level reference is reassigned.\n",
    "\n",
    "That is, the pandas code might look something like this\n",
    "\n",
    "```python\n",
    "def dataframe_method(self, inplace=False):\n",
    "    data = self.copy()  # regardless of inplace\n",
    "    result = ...\n",
    "    if inplace:\n",
    "        self._update_inplace(data)\n",
    "    else:\n",
    "        return result\n",
    "\n",
    "```\n",
    "\n",
    "There's a lot of defensive copying in pandas.\n",
    "Part of this comes down to pandas being built on top of NumPy, and not having full control over how memory is handled and shared.\n",
    "We saw it above when we defined our own functions `extract_city_name` and `time_to_datetime`.\n",
    "Without the `copy`, adding the columns would modify the input DataFrame, which just isn't polite.\n",
    "\n",
    "Finally, inplace operations don't make sense in projects like [ibis](http://www.ibis-project.org) or [dask](http://dask.pydata.org/en/latest/), where you're manipulating expressions or building up a DAG of tasks to be executed, rather than manipulating the data directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application\n",
    "\n",
    "I feel like we haven't done much coding, mostly just me shouting from the top of a soapbox (sorry about that).\n",
    "Let's do some exploratory analysis.\n",
    "\n",
    "What does the daily flight pattern look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ClminrTGk6LmwPEgkcBMCmvaC9rlJgeoeQnfILO0O7lqgaa/ofk+LEkEi+y5k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hszpoQtjiOQFgvCmHs7IUADRnaL1o0IzpmPiPvw9oX62aHudkQWIEuDhTvPn8LmTNjEGQXMKm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\nSER12kneGZ6VDiIMOH7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Pn8QEpkj8u42w1NXBbrZAJBEj9Npr4O20F6IdDpT863tU/PQ/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\nTz8NsViM1157DQCg1Wpx8OBBnD17Fr6+vnjiiSfcsax+Q+j6AOF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11768fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.dropna(subset=['dep_time', 'unique_carrier'])\n",
    "   .loc[df['unique_carrier']\n",
    "       .isin(df['unique_carrier'].value_counts().index[:5])]\n",
    "   .set_index('dep_time')\n",
    "   # TimeGrouper to resample & groupby at once\n",
    "   .groupby(['unique_carrier', pd.TimeGrouper(\"H\")])\n",
    "   .fl_num.count()\n",
    "   .unstack(0)\n",
    "   .fillna(0)\n",
    "   .rolling(24)\n",
    "   .sum()\n",
    "   .rename_axis(\"Flights per Day\", axis=1)\n",
    "   .plot()\n",
    ")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/taugspurger/miniconda3/envs/modern-pandas/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does a plane with multiple flights on the same day get backed up, causing later flights to be delayed more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G/bywJwpIAAAAIEjaJLbVWS07WB0DMA1TWAEAAAAAhjACCUdgdTQAAACg+Qy/Cl28eLFG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\nmujSXaWE8hYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175d5048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = 'png'\n",
    "flights = (df[['fl_date', 'tail_num', 'dep_time', 'dep_delay']]\n",
    "           .dropna()\n",
    "           .sort_values('dep_time')\n",
    "           .loc[lambda x: x.dep_delay < 500]\n",
    "           .assign(turn = lambda x:\n",
    "                x.groupby(['fl_date', 'tail_num'])\n",
    "                 .dep_time\n",
    "                 .transform('rank').astype(int)))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.boxplot(x='turn', y='dep_delay', data=flights, ax=ax)\n",
    "ax.set_ylim(-50, 50)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doesn't really look like it. Maybe other planes are swapped in when one gets delayed,\n",
    "but we don't have data on *scheduled* flights per plane.\n",
    "\n",
    "Do flights later in the day have longer delays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OTsaBAwfw7LPP4kc/+hH++c9/AuhbnbGurg779u3Dr371K2zbto13RHyoPoBoeHyP9Em5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\nERERERER6YQbSCIiIiIiItLJ/wGcvLhQoaQP6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141392390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "(df[['fl_date', 'tail_num', 'dep_time', 'dep_delay']]\n",
    "    .dropna()\n",
    "    .assign(hour=lambda x: x.dep_time.dt.hour)\n",
    "    .query('5 < dep_delay < 600')\n",
    "    .pipe((sns.boxplot, 'data'), 'hour', 'dep_delay'))\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There could be something here. I didn't show it here since I filtered them out,\n",
    "but the vast majority of flights do leave on time.\n",
    "\n",
    "Thanks for reading!\n",
    "This section was a bit more abstract, since we were talking about styles\n",
    "of coding rather than how to actually accomplish tasks.\n",
    "I'm sometimes guilty of putting too much work into making my data wrangling code look nice and feel correct, at the expense of actually analyzing the data.\n",
    "This isn't a competition to have the best or cleanest pandas code; pandas is always just a means to the end that is your research or business problem.\n",
    "Thanks for indulging me.\n",
    "Next time we'll talk about a much more practical topic: performance."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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